Overview

Dataset statistics

Number of variables40
Number of observations1815
Missing cells2034
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory567.3 KiB
Average record size in memory320.1 B

Variable types

Numeric7
Text6
Categorical27

Alerts

hierarchy_1 has constant value ""Constant
BUMPS has constant value ""Constant
INGROWNS has constant value ""Constant
UNEVEN TONE has constant value ""Constant
ACNE is highly imbalanced (67.0%)Imbalance
BLEMISHES is highly imbalanced (65.9%)Imbalance
OILINESS is highly imbalanced (66.4%)Imbalance
MASK is highly imbalanced (99.3%)Imbalance
DARK SPOTS is highly imbalanced (65.9%)Imbalance
PUFFINESS is highly imbalanced (83.0%)Imbalance
DARK CIRCLES is highly imbalanced (78.5%)Imbalance
child_sku has 1478 (81.4%) missing valuesMissing
size_oz has 307 (16.9%) missing valuesMissing
hierarchy_3 has 235 (12.9%) missing valuesMissing
item_sku has unique valuesUnique
connections_num has 58 (3.2%) zerosZeros

Reproduction

Analysis started2024-02-26 22:09:04.340335
Analysis finished2024-02-26 22:10:39.120636
Duration1 minute and 34.78 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

item_sku
Real number (ℝ)

UNIQUE 

Distinct1815
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2313892.7
Minimum101220
Maximum2743250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:39.254852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum101220
5-th percentile1580799.6
Q12184959
median2425098
Q32551178
95-th percentile2659177
Maximum2743250
Range2642030
Interquartile range (IQR)366219

Descriptive statistics

Standard deviation342238.48
Coefficient of variation (CV)0.14790594
Kurtosis3.2694002
Mean2313892.7
Median Absolute Deviation (MAD)165148
Skewness-1.6287426
Sum4.1997153 × 109
Variance1.1712718 × 1011
MonotonicityNot monotonic
2024-02-26T14:10:39.393481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101220 1
 
0.1%
2519775 1
 
0.1%
2530632 1
 
0.1%
2530426 1
 
0.1%
2530384 1
 
0.1%
2529675 1
 
0.1%
2529659 1
 
0.1%
2529642 1
 
0.1%
2529576 1
 
0.1%
2529287 1
 
0.1%
Other values (1805) 1805
99.4%
ValueCountFrequency (%)
101220 1
0.1%
126581 1
0.1%
1027465 1
0.1%
1027473 1
0.1%
1027507 1
0.1%
1064062 1
0.1%
1066711 1
0.1%
1066729 1
0.1%
1100742 1
0.1%
1108836 1
0.1%
ValueCountFrequency (%)
2743250 1
0.1%
2735132 1
0.1%
2678555 1
0.1%
2678233 1
0.1%
2677896 1
0.1%
2677862 1
0.1%
2677607 1
0.1%
2677425 1
0.1%
2677409 1
0.1%
2677383 1
0.1%

brand
Text

Distinct136
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:39.723508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length25
Median length18
Mean length11.061708
Min length3

Characters and Unicode

Total characters20077
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)1.4%

Sample

1st rowSHISEIDO
2nd rowCLINIQUE
3rd rowCLINIQUE
4th rowCLINIQUE
5th rowSHISEIDO
ValueCountFrequency (%)
the 114
 
3.6%
dr 112
 
3.6%
skincare 93
 
3.0%
clinique 67
 
2.1%
beauty 56
 
1.8%
sephora 50
 
1.6%
dermalogica 49
 
1.6%
collection 49
 
1.6%
skin 47
 
1.5%
shiseido 45
 
1.4%
Other values (195) 2468
78.3%
2024-02-26T14:10:40.142315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2017
 
10.0%
A 1741
 
8.7%
I 1549
 
7.7%
R 1515
 
7.5%
1335
 
6.6%
S 1307
 
6.5%
O 1148
 
5.7%
T 1135
 
5.7%
N 1102
 
5.5%
L 947
 
4.7%
Other values (28) 6281
31.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18285
91.1%
Space Separator 1335
 
6.6%
Other Punctuation 232
 
1.2%
Decimal Number 166
 
0.8%
Dash Punctuation 33
 
0.2%
Math Symbol 26
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2017
11.0%
A 1741
 
9.5%
I 1549
 
8.5%
R 1515
 
8.3%
S 1307
 
7.1%
O 1148
 
6.3%
T 1135
 
6.2%
N 1102
 
6.0%
L 947
 
5.2%
C 902
 
4.9%
Other values (18) 4922
26.9%
Decimal Number
ValueCountFrequency (%)
1 78
47.0%
8 44
26.5%
5 39
23.5%
2 5
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 136
58.6%
' 68
29.3%
! 28
 
12.1%
Space Separator
ValueCountFrequency (%)
1335
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%
Math Symbol
ValueCountFrequency (%)
+ 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18285
91.1%
Common 1792
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2017
11.0%
A 1741
 
9.5%
I 1549
 
8.5%
R 1515
 
8.3%
S 1307
 
7.1%
O 1148
 
6.3%
T 1135
 
6.2%
N 1102
 
6.0%
L 947
 
5.2%
C 902
 
4.9%
Other values (18) 4922
26.9%
Common
ValueCountFrequency (%)
1335
74.5%
. 136
 
7.6%
1 78
 
4.4%
' 68
 
3.8%
8 44
 
2.5%
5 39
 
2.2%
- 33
 
1.8%
! 28
 
1.6%
+ 26
 
1.5%
2 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20036
99.8%
None 41
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2017
 
10.1%
A 1741
 
8.7%
I 1549
 
7.7%
R 1515
 
7.6%
1335
 
6.7%
S 1307
 
6.5%
O 1148
 
5.7%
T 1135
 
5.7%
N 1102
 
5.5%
L 947
 
4.7%
Other values (26) 6240
31.1%
None
ValueCountFrequency (%)
É 23
56.1%
Ô 18
43.9%

product
Text

Distinct1808
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:40.495058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length108
Median length73
Mean length40.806612
Min length6

Characters and Unicode

Total characters74064
Distinct characters65
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1801 ?
Unique (%)99.2%

Sample

1st rowFACIAL COTTON
2nd rowACNE SOLUTIONS ALL-OVER CLEARING TREATMENT OIL-FREE
3rd rowACNE SOLUTIONSâ„¢ CLARIFYING LOTION
4th rowACNE SOLUTIONSâ„¢ CLEANSING FOAM
5th rowBENEFIANCE NUTRIPERFECT NIGHT CREAM
ValueCountFrequency (%)
383
 
3.5%
serum 273
 
2.5%
with 267
 
2.4%
cream 238
 
2.2%
moisturizer 197
 
1.8%
face 186
 
1.7%
eye 166
 
1.5%
acid 155
 
1.4%
spf 143
 
1.3%
vitamin 140
 
1.3%
Other values (1709) 8785
80.4%
2024-02-26T14:10:41.115315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9156
12.4%
E 7235
 
9.8%
I 6184
 
8.3%
A 5221
 
7.0%
R 5094
 
6.9%
N 4601
 
6.2%
T 4163
 
5.6%
L 3314
 
4.5%
S 3285
 
4.4%
O 3232
 
4.4%
Other values (55) 22579
30.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 62763
84.7%
Space Separator 9160
 
12.4%
Decimal Number 637
 
0.9%
Dash Punctuation 473
 
0.6%
Other Punctuation 410
 
0.6%
Other Symbol 275
 
0.4%
Math Symbol 271
 
0.4%
Control 49
 
0.1%
Final Punctuation 8
 
< 0.1%
Open Punctuation 7
 
< 0.1%
Other values (4) 11
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 7235
11.5%
I 6184
 
9.9%
A 5221
 
8.3%
R 5094
 
8.1%
N 4601
 
7.3%
T 4163
 
6.6%
L 3314
 
5.3%
S 3285
 
5.2%
O 3232
 
5.1%
C 3050
 
4.9%
Other values (19) 17384
27.7%
Decimal Number
ValueCountFrequency (%)
0 203
31.9%
1 97
15.2%
5 88
13.8%
2 83
13.0%
3 79
 
12.4%
4 47
 
7.4%
8 13
 
2.0%
7 12
 
1.9%
6 8
 
1.3%
9 7
 
1.1%
Other Punctuation
ValueCountFrequency (%)
& 162
39.5%
% 126
30.7%
. 54
 
13.2%
, 30
 
7.3%
/ 18
 
4.4%
' 9
 
2.2%
: 9
 
2.2%
* 2
 
0.5%
Space Separator
ValueCountFrequency (%)
9156
> 99.9%
  2
 
< 0.1%
  2
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
â„¢ 197
71.6%
® 75
 
27.3%
° 3
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 470
99.4%
– 3
 
0.6%
Math Symbol
ValueCountFrequency (%)
+ 270
99.6%
| 1
 
0.4%
Modifier Letter
ValueCountFrequency (%)
áµ€ 1
50.0%
á´¹ 1
50.0%
Control
ValueCountFrequency (%)
49
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Modifier Symbol
ValueCountFrequency (%)
Ëš 1
100.0%
Format
ValueCountFrequency (%)
​ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62765
84.7%
Common 11299
 
15.3%

Most frequent character per script

Common
ValueCountFrequency (%)
9156
81.0%
- 470
 
4.2%
+ 270
 
2.4%
0 203
 
1.8%
â„¢ 197
 
1.7%
& 162
 
1.4%
% 126
 
1.1%
1 97
 
0.9%
5 88
 
0.8%
2 83
 
0.7%
Other values (24) 447
 
4.0%
Latin
ValueCountFrequency (%)
E 7235
11.5%
I 6184
 
9.9%
A 5221
 
8.3%
R 5094
 
8.1%
N 4601
 
7.3%
T 4163
 
6.6%
L 3314
 
5.3%
S 3285
 
5.2%
O 3232
 
5.1%
C 3050
 
4.9%
Other values (21) 17386
27.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73736
99.6%
Letterlike Symbols 197
 
0.3%
None 114
 
0.2%
Punctuation 14
 
< 0.1%
Phonetic Ext 2
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9156
12.4%
E 7235
 
9.8%
I 6184
 
8.4%
A 5221
 
7.1%
R 5094
 
6.9%
N 4601
 
6.2%
T 4163
 
5.6%
L 3314
 
4.5%
S 3285
 
4.5%
O 3232
 
4.4%
Other values (41) 22251
30.2%
Letterlike Symbols
ValueCountFrequency (%)
â„¢ 197
100.0%
None
ValueCountFrequency (%)
® 75
65.8%
È 19
 
16.7%
É 14
 
12.3%
° 3
 
2.6%
  2
 
1.8%
Ç 1
 
0.9%
Punctuation
ValueCountFrequency (%)
’ 8
57.1%
– 3
 
21.4%
  2
 
14.3%
​ 1
 
7.1%
Phonetic Ext
ValueCountFrequency (%)
áµ€ 1
50.0%
á´¹ 1
50.0%
Modifier Letters
ValueCountFrequency (%)
Ëš 1
100.0%

rating
Real number (ℝ)

Distinct1445
Distinct (%)79.9%
Missing7
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean4.2143988
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:41.302360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.4
Q14.016
median4.30205
Q34.4978
95-th percentile4.776705
Maximum5
Range4
Interquartile range (IQR)0.4818

Descriptive statistics

Standard deviation0.45785481
Coefficient of variation (CV)0.1086406
Kurtosis7.7244859
Mean4.2143988
Median Absolute Deviation (MAD)0.236
Skewness-1.9187264
Sum7619.633
Variance0.20963102
MonotonicityNot monotonic
2024-02-26T14:10:41.441616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 25
 
1.4%
5 23
 
1.3%
3.6667 12
 
0.7%
4.5 11
 
0.6%
4.25 7
 
0.4%
4.375 6
 
0.3%
3.5 6
 
0.3%
3.75 6
 
0.3%
4.3333 6
 
0.3%
4.2 5
 
0.3%
Other values (1435) 1701
93.7%
(Missing) 7
 
0.4%
ValueCountFrequency (%)
1 4
0.2%
1.7143 1
 
0.1%
1.75 1
 
0.1%
1.8 2
0.1%
2.1429 1
 
0.1%
2.1818 1
 
0.1%
2.3636 1
 
0.1%
2.4138 1
 
0.1%
2.5 2
0.1%
2.5217 1
 
0.1%
ValueCountFrequency (%)
5 23
1.3%
4.9286 1
 
0.1%
4.92 1
 
0.1%
4.9167 1
 
0.1%
4.9074 1
 
0.1%
4.8947 1
 
0.1%
4.8913 1
 
0.1%
4.881 1
 
0.1%
4.8725 1
 
0.1%
4.8712 1
 
0.1%

loves_count
Real number (ℝ)

Distinct1771
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39181.125
Minimum95
Maximum1651415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:41.585437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile1928.7
Q16314.5
median14642
Q338271
95-th percentile145481.1
Maximum1651415
Range1651320
Interquartile range (IQR)31956.5

Descriptive statistics

Standard deviation89552.012
Coefficient of variation (CV)2.2855906
Kurtosis114.8152
Mean39181.125
Median Absolute Deviation (MAD)10733
Skewness8.7878523
Sum71113741
Variance8.0195628 × 109
MonotonicityNot monotonic
2024-02-26T14:10:41.727748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4056 2
 
0.1%
2740 2
 
0.1%
70433 2
 
0.1%
2869 2
 
0.1%
28501 2
 
0.1%
10426 2
 
0.1%
1646 2
 
0.1%
2928 2
 
0.1%
9640 2
 
0.1%
11442 2
 
0.1%
Other values (1761) 1795
98.9%
ValueCountFrequency (%)
95 1
0.1%
328 1
0.1%
379 1
0.1%
406 1
0.1%
413 1
0.1%
429 1
0.1%
590 1
0.1%
594 1
0.1%
632 1
0.1%
649 1
0.1%
ValueCountFrequency (%)
1651415 1
0.1%
1429910 1
0.1%
1041090 1
0.1%
995264 1
0.1%
743843 1
0.1%
722270 1
0.1%
712872 1
0.1%
609214 1
0.1%
577201 1
0.1%
564701 1
0.1%

reviews_num
Real number (ℝ)

Distinct824
Distinct (%)45.6%
Missing7
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean539.36449
Minimum1
Maximum18361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:41.871472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q165.75
median221
Q3569
95-th percentile2092.9
Maximum18361
Range18360
Interquartile range (IQR)503.25

Descriptive statistics

Standard deviation982.515
Coefficient of variation (CV)1.821616
Kurtosis73.954246
Mean539.36449
Median Absolute Deviation (MAD)190
Skewness6.245693
Sum975171
Variance965335.73
MonotonicityNot monotonic
2024-02-26T14:10:42.009532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 15
 
0.8%
4 14
 
0.8%
43 14
 
0.8%
12 14
 
0.8%
5 14
 
0.8%
6 13
 
0.7%
23 12
 
0.7%
33 12
 
0.7%
14 12
 
0.7%
8 11
 
0.6%
Other values (814) 1677
92.4%
ValueCountFrequency (%)
1 7
0.4%
2 11
0.6%
3 11
0.6%
4 14
0.8%
5 14
0.8%
6 13
0.7%
7 15
0.8%
8 11
0.6%
9 11
0.6%
10 8
0.4%
ValueCountFrequency (%)
18361 1
0.1%
10031 1
0.1%
7545 1
0.1%
7224 1
0.1%
7179 1
0.1%
6626 1
0.1%
6616 1
0.1%
5863 2
0.1%
5858 1
0.1%
5711 1
0.1%

price
Real number (ℝ)

Distinct202
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.959983
Minimum3
Maximum1900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:42.142380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile12
Q129
median45
Q372
95-th percentile176.2
Maximum1900
Range1897
Interquartile range (IQR)43

Descriptive statistics

Standard deviation74.191487
Coefficient of variation (CV)1.1783911
Kurtosis212.60555
Mean62.959983
Median Absolute Deviation (MAD)20
Skewness10.134338
Sum114272.37
Variance5504.3767
MonotonicityNot monotonic
2024-02-26T14:10:42.268649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 62
 
3.4%
30 49
 
2.7%
28 42
 
2.3%
48 40
 
2.2%
35 40
 
2.2%
25 37
 
2.0%
36 36
 
2.0%
44 36
 
2.0%
39 36
 
2.0%
65 35
 
1.9%
Other values (192) 1402
77.2%
ValueCountFrequency (%)
3 1
 
0.1%
4 3
 
0.2%
5 3
 
0.2%
6 9
0.5%
6.4 1
 
0.1%
6.6 1
 
0.1%
6.7 2
 
0.1%
7 8
0.4%
7.5 1
 
0.1%
7.8 1
 
0.1%
ValueCountFrequency (%)
1900 1
0.1%
575 1
0.1%
495 2
0.1%
455 2
0.1%
425 1
0.1%
400 2
0.1%
399 2
0.1%
395 2
0.1%
380 1
0.1%
375 1
0.1%

child_sku
Text

MISSING 

Distinct337
Distinct (%)100.0%
Missing1478
Missing (%)81.4%
Memory size14.3 KiB
2024-02-26T14:10:42.489359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length87
Median length7
Mean length12.074184
Min length6

Characters and Unicode

Total characters4069
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique337 ?
Unique (%)100.0%

Sample

1st row1880350
2nd row2531747
3rd row2100642
4th row1284629,2533065
5th row2480903,1543313
ValueCountFrequency (%)
2499697 1
 
0.3%
1295658 1
 
0.3%
2531747 1
 
0.3%
2100642 1
 
0.3%
1284629,2533065 1
 
0.3%
2480903,1543313 1
 
0.3%
1309590,1784826 1
 
0.3%
564278 1
 
0.3%
711283 1
 
0.3%
2421840,2073195 1
 
0.3%
Other values (327) 327
97.0%
2024-02-26T14:10:42.840487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 777
19.1%
6 402
9.9%
5 397
9.8%
4 366
9.0%
1 356
8.7%
3 347
8.5%
8 324
8.0%
7 318
7.8%
9 305
 
7.5%
0 263
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3855
94.7%
Other Punctuation 214
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 777
20.2%
6 402
10.4%
5 397
10.3%
4 366
9.5%
1 356
9.2%
3 347
9.0%
8 324
8.4%
7 318
8.2%
9 305
 
7.9%
0 263
 
6.8%
Other Punctuation
ValueCountFrequency (%)
, 214
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4069
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 777
19.1%
6 402
9.9%
5 397
9.8%
4 366
9.0%
1 356
8.7%
3 347
8.5%
8 324
8.0%
7 318
7.8%
9 305
 
7.5%
0 263
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 777
19.1%
6 402
9.9%
5 397
9.8%
4 366
9.0%
1 356
8.7%
3 347
8.5%
8 324
8.0%
7 318
7.8%
9 305
 
7.5%
0 263
 
6.5%

item_id
Text

Distinct1814
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:43.223365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9922865
Min length5

Characters and Unicode

Total characters12691
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1813 ?
Unique (%)99.9%

Sample

1st rowP173726
2nd rowP188306
3rd rowP188307
4th rowP188309
5th rowP202935
ValueCountFrequency (%)
p500744 2
 
0.1%
p375864 1
 
0.1%
p188309 1
 
0.1%
p202935 1
 
0.1%
p201439 1
 
0.1%
p201440 1
 
0.1%
p217513 1
 
0.1%
p217932 1
 
0.1%
p232327 1
 
0.1%
p232902 1
 
0.1%
Other values (1804) 1804
99.4%
2024-02-26T14:10:43.741912image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 2192
17.3%
P 1815
14.3%
0 1266
10.0%
5 1256
9.9%
2 971
7.7%
3 970
7.6%
7 955
7.5%
1 901
7.1%
6 887
7.0%
8 806
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10876
85.7%
Uppercase Letter 1815
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 2192
20.2%
0 1266
11.6%
5 1256
11.5%
2 971
8.9%
3 970
8.9%
7 955
8.8%
1 901
8.3%
6 887
8.2%
8 806
 
7.4%
9 672
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
P 1815
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10876
85.7%
Latin 1815
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
4 2192
20.2%
0 1266
11.6%
5 1256
11.5%
2 971
8.9%
3 970
8.9%
7 955
8.8%
1 901
8.3%
6 887
8.2%
8 806
 
7.4%
9 672
 
6.2%
Latin
ValueCountFrequency (%)
P 1815
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 2192
17.3%
P 1815
14.3%
0 1266
10.0%
5 1256
9.9%
2 971
7.7%
3 970
7.6%
7 955
7.5%
1 901
7.1%
6 887
7.0%
8 806
 
6.4%

similar_products
Text

Distinct1801
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:44.150565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length39
Median length39
Mean length38.903581
Min length34

Characters and Unicode

Total characters70610
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1794 ?
Unique (%)98.8%

Sample

1st row2735132,2031391,2191526,2698348,2031441
2nd row1027473,1677137,1027507,1592831,1592856
3rd row1027465,1027507,1677137,1592831,1802321
4th row1027473,1027465,1677137,1592831,2531747
5th row2234102,2234110,1723865,2733681,1723857
ValueCountFrequency (%)
2735132,2348431,2743250,2556744,2031391 8
 
0.4%
2743250,2534899,2534907,2535656,2620227 3
 
0.2%
2743250,2420339,2673036,2673044,1802438 2
 
0.1%
2345205,2345189,2283034,2373918,2110971 2
 
0.1%
2549731,2458446,2113132,2405819,2429884 2
 
0.1%
2480820,2568871,2258739,2122257,2480812 2
 
0.1%
2458628,2458644,2594265,2561322,2645216 2
 
0.1%
1066711,1717016,1484641,2531697,2664365 1
 
0.1%
2532190,1723865,2733681,1723857,2640647 1
 
0.1%
1167196,2269983,1826155,1932284,2576122 1
 
0.1%
Other values (1791) 1791
98.7%
2024-02-26T14:10:44.651781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 13084
18.5%
, 7260
10.3%
3 6724
9.5%
4 6593
9.3%
1 6305
8.9%
6 6097
8.6%
5 5686
8.1%
7 5031
 
7.1%
0 4876
 
6.9%
8 4621
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63350
89.7%
Other Punctuation 7260
 
10.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 13084
20.7%
3 6724
10.6%
4 6593
10.4%
1 6305
10.0%
6 6097
9.6%
5 5686
9.0%
7 5031
 
7.9%
0 4876
 
7.7%
8 4621
 
7.3%
9 4333
 
6.8%
Other Punctuation
ValueCountFrequency (%)
, 7260
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70610
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 13084
18.5%
, 7260
10.3%
3 6724
9.5%
4 6593
9.3%
1 6305
8.9%
6 6097
8.6%
5 5686
8.1%
7 5031
 
7.1%
0 4876
 
6.9%
8 4621
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 13084
18.5%
, 7260
10.3%
3 6724
9.5%
4 6593
9.3%
1 6305
8.9%
6 6097
8.6%
5 5686
8.1%
7 5031
 
7.1%
0 4876
 
6.9%
8 4621
 
6.5%

size_oz
Real number (ℝ)

MISSING 

Distinct140
Distinct (%)9.3%
Missing307
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean2.8182599
Minimum0.01
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:44.813357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.5
Q11
median1.7
Q33.4
95-th percentile6.7
Maximum94
Range93.99
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation5.800095
Coefficient of variation (CV)2.0580412
Kurtosis114.09741
Mean2.8182599
Median Absolute Deviation (MAD)0.7
Skewness9.7172287
Sum4249.936
Variance33.641102
MonotonicityNot monotonic
2024-02-26T14:10:44.940419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 311
17.1%
1.7 269
14.8%
0.5 150
 
8.3%
5 87
 
4.8%
2 56
 
3.1%
4 46
 
2.5%
1.69 43
 
2.4%
3.4 40
 
2.2%
1.6 30
 
1.7%
6.7 28
 
1.5%
Other values (130) 448
24.7%
(Missing) 307
16.9%
ValueCountFrequency (%)
0.01 1
 
0.1%
0.05 2
 
0.1%
0.095 1
 
0.1%
0.11 1
 
0.1%
0.12 3
0.2%
0.13 2
 
0.1%
0.135 1
 
0.1%
0.14 5
0.3%
0.15 2
 
0.1%
0.2 4
0.2%
ValueCountFrequency (%)
94 1
 
0.1%
75 1
 
0.1%
68 5
0.3%
54 1
 
0.1%
35 2
 
0.1%
34 2
 
0.1%
28 1
 
0.1%
27 2
 
0.1%
21 1
 
0.1%
19 1
 
0.1%

hierarchy_1
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
SKINCARE
1815 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters14520
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSKINCARE
2nd rowSKINCARE
3rd rowSKINCARE
4th rowSKINCARE
5th rowSKINCARE

Common Values

ValueCountFrequency (%)
SKINCARE 1815
100.0%

Length

2024-02-26T14:10:45.046088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:45.150467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
skincare 1815
100.0%

Most occurring characters

ValueCountFrequency (%)
S 1815
12.5%
K 1815
12.5%
I 1815
12.5%
N 1815
12.5%
C 1815
12.5%
A 1815
12.5%
R 1815
12.5%
E 1815
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 14520
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 1815
12.5%
K 1815
12.5%
I 1815
12.5%
N 1815
12.5%
C 1815
12.5%
A 1815
12.5%
R 1815
12.5%
E 1815
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 14520
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1815
12.5%
K 1815
12.5%
I 1815
12.5%
N 1815
12.5%
C 1815
12.5%
A 1815
12.5%
R 1815
12.5%
E 1815
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1815
12.5%
K 1815
12.5%
I 1815
12.5%
N 1815
12.5%
C 1815
12.5%
A 1815
12.5%
R 1815
12.5%
E 1815
12.5%

hierarchy_2
Categorical

Distinct12
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
MOISTURIZERS
437 
TREATMENTS
361 
CLEANSERS
297 
EYE CARE
150 
MASKS
117 
Other values (7)
453 

Length

Max length22
Median length17
Mean length10.585675
Min length5

Characters and Unicode

Total characters19213
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLEANSERS
2nd rowTREATMENTS
3rd rowCLEANSERS
4th rowCLEANSERS
5th rowMOISTURIZERS

Common Values

ValueCountFrequency (%)
MOISTURIZERS 437
24.1%
TREATMENTS 361
19.9%
CLEANSERS 297
16.4%
EYE CARE 150
 
8.3%
MASKS 117
 
6.4%
VALUE & GIFT SETS 103
 
5.7%
SUNSCREEN 92
 
5.1%
MINI SIZE 76
 
4.2%
HIGH TECH TOOLS 54
 
3.0%
LIP BALMS & TREATMENTS 46
 
2.5%
Other values (2) 82
 
4.5%

Length

2024-02-26T14:10:45.249279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
moisturizers 437
16.6%
treatments 407
15.4%
cleansers 297
11.3%
eye 150
 
5.7%
care 150
 
5.7%
149
 
5.7%
masks 117
 
4.4%
value 103
 
3.9%
gift 103
 
3.9%
sets 103
 
3.9%
Other values (11) 620
23.5%

Most occurring characters

ValueCountFrequency (%)
E 2979
15.5%
S 2839
14.8%
T 2012
10.5%
R 1860
9.7%
I 1305
 
6.8%
A 1160
 
6.0%
N 1086
 
5.7%
M 1083
 
5.6%
821
 
4.3%
L 670
 
3.5%
Other values (14) 3398
17.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18243
95.0%
Space Separator 821
 
4.3%
Other Punctuation 149
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2979
16.3%
S 2839
15.6%
T 2012
11.0%
R 1860
10.2%
I 1305
7.2%
A 1160
 
6.4%
N 1086
 
6.0%
M 1083
 
5.9%
L 670
 
3.7%
U 632
 
3.5%
Other values (12) 2617
14.3%
Space Separator
ValueCountFrequency (%)
821
100.0%
Other Punctuation
ValueCountFrequency (%)
& 149
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18243
95.0%
Common 970
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2979
16.3%
S 2839
15.6%
T 2012
11.0%
R 1860
10.2%
I 1305
7.2%
A 1160
 
6.4%
N 1086
 
6.0%
M 1083
 
5.9%
L 670
 
3.7%
U 632
 
3.5%
Other values (12) 2617
14.3%
Common
ValueCountFrequency (%)
821
84.6%
& 149
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19213
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2979
15.5%
S 2839
14.8%
T 2012
10.5%
R 1860
9.7%
I 1305
 
6.8%
A 1160
 
6.0%
N 1086
 
5.7%
M 1083
 
5.6%
821
 
4.3%
L 670
 
3.5%
Other values (14) 3398
17.7%

hierarchy_3
Categorical

MISSING 

Distinct30
Distinct (%)1.9%
Missing235
Missing (%)12.9%
Memory size14.3 KiB
MOISTURIZERS
311 
FACE SERUMS
297 
FACE WASH & CLEANSERS
185 
EYE CREAMS & TREATMENTS
140 
FACE MASKS
91 
Other values (25)
556 

Length

Max length25
Median length23
Mean length13.832911
Min length6

Characters and Unicode

Total characters21856
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAKEUP REMOVERS
2nd rowBLEMISH & ACNE TREATMENTS
3rd rowTONERS
4th rowFACE WASH & CLEANSERS
5th rowMOISTURIZERS

Common Values

ValueCountFrequency (%)
MOISTURIZERS 311
17.1%
FACE SERUMS 297
16.4%
FACE WASH & CLEANSERS 185
10.2%
EYE CREAMS & TREATMENTS 140
7.7%
FACE MASKS 91
 
5.0%
FACE SUNSCREEN 80
 
4.4%
TONERS 66
 
3.6%
FACE OILS 54
 
3.0%
MISTS & ESSENCES 47
 
2.6%
ANTI-AGING 39
 
2.1%
Other values (20) 270
14.9%
(Missing) 235
12.9%

Length

2024-02-26T14:10:45.420382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
face 724
20.8%
406
11.6%
moisturizers 311
8.9%
serums 297
 
8.5%
wash 185
 
5.3%
cleansers 185
 
5.3%
treatments 166
 
4.8%
creams 160
 
4.6%
eye 151
 
4.3%
masks 127
 
3.6%
Other values (36) 776
22.2%

Most occurring characters

ValueCountFrequency (%)
E 3250
14.9%
S 3109
14.2%
1908
8.7%
A 1853
8.5%
R 1729
 
7.9%
C 1327
 
6.1%
M 1177
 
5.4%
T 1085
 
5.0%
I 968
 
4.4%
F 855
 
3.9%
Other values (17) 4595
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 19503
89.2%
Space Separator 1908
 
8.7%
Other Punctuation 406
 
1.9%
Dash Punctuation 39
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3250
16.7%
S 3109
15.9%
A 1853
9.5%
R 1729
8.9%
C 1327
 
6.8%
M 1177
 
6.0%
T 1085
 
5.6%
I 968
 
5.0%
F 855
 
4.4%
N 810
 
4.2%
Other values (14) 3340
17.1%
Space Separator
ValueCountFrequency (%)
1908
100.0%
Other Punctuation
ValueCountFrequency (%)
& 406
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19503
89.2%
Common 2353
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3250
16.7%
S 3109
15.9%
A 1853
9.5%
R 1729
8.9%
C 1327
 
6.8%
M 1177
 
6.0%
T 1085
 
5.6%
I 968
 
5.0%
F 855
 
4.4%
N 810
 
4.2%
Other values (14) 3340
17.1%
Common
ValueCountFrequency (%)
1908
81.1%
& 406
 
17.3%
- 39
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 3250
14.9%
S 3109
14.2%
1908
8.7%
A 1853
8.5%
R 1729
 
7.9%
C 1327
 
6.1%
M 1177
 
5.4%
T 1085
 
5.0%
I 968
 
4.4%
F 855
 
3.9%
Other values (17) 4595
21.0%

SKIN_CONCERN_UNKNOWN
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
1
959 
0
856 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 959
52.8%
0 856
47.2%

Length

2024-02-26T14:10:45.564180image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:45.652260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 959
52.8%
0 856
47.2%

Most occurring characters

ValueCountFrequency (%)
1 959
52.8%
0 856
47.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 959
52.8%
0 856
47.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 959
52.8%
0 856
47.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 959
52.8%
0 856
47.2%

ACNE
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1705 
1
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1705
93.9%
1 110
 
6.1%

Length

2024-02-26T14:10:45.743350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:45.831550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1705
93.9%
1 110
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 1705
93.9%
1 110
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1705
93.9%
1 110
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1705
93.9%
1 110
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1705
93.9%
1 110
 
6.1%

BLEMISHES
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1700 
1
 
115

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

Length

2024-02-26T14:10:45.925161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:46.006392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

OILINESS
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1702 
1
 
113

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1702
93.8%
1 113
 
6.2%

Length

2024-02-26T14:10:46.118550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:46.213506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1702
93.8%
1 113
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 1702
93.8%
1 113
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1702
93.8%
1 113
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1702
93.8%
1 113
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1702
93.8%
1 113
 
6.2%

PORES
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1611 
1
204 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1611
88.8%
1 204
 
11.2%

Length

2024-02-26T14:10:46.310150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:46.412607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1611
88.8%
1 204
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 1611
88.8%
1 204
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1611
88.8%
1 204
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1611
88.8%
1 204
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1611
88.8%
1 204
 
11.2%

FINE LINES
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1455 
1
360 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1455
80.2%
1 360
 
19.8%

Length

2024-02-26T14:10:46.527293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:46.644670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1455
80.2%
1 360
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 1455
80.2%
1 360
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1455
80.2%
1 360
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1455
80.2%
1 360
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1455
80.2%
1 360
 
19.8%

WRINKLES
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1457 
1
358 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1457
80.3%
1 358
 
19.7%

Length

2024-02-26T14:10:46.736115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:46.828935image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1457
80.3%
1 358
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0 1457
80.3%
1 358
 
19.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1457
80.3%
1 358
 
19.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1457
80.3%
1 358
 
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1457
80.3%
1 358
 
19.7%

DULLNESS
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1465 
1
350 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1465
80.7%
1 350
 
19.3%

Length

2024-02-26T14:10:47.223558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:47.321920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1465
80.7%
1 350
 
19.3%

Most occurring characters

ValueCountFrequency (%)
0 1465
80.7%
1 350
 
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1465
80.7%
1 350
 
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1465
80.7%
1 350
 
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1465
80.7%
1 350
 
19.3%

FIRMNESS
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1614 
1
201 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1614
88.9%
1 201
 
11.1%

Length

2024-02-26T14:10:47.420449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:47.508702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1614
88.9%
1 201
 
11.1%

Most occurring characters

ValueCountFrequency (%)
0 1614
88.9%
1 201
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1614
88.9%
1 201
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1614
88.9%
1 201
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1614
88.9%
1 201
 
11.1%

ELASTICITY
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1613 
1
202 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1613
88.9%
1 202
 
11.1%

Length

2024-02-26T14:10:47.597551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:47.687809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1613
88.9%
1 202
 
11.1%

Most occurring characters

ValueCountFrequency (%)
0 1613
88.9%
1 202
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1613
88.9%
1 202
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1613
88.9%
1 202
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1613
88.9%
1 202
 
11.1%

UNEVEN TEXTURE
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1544 
1
271 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1544
85.1%
1 271
 
14.9%

Length

2024-02-26T14:10:47.801735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:47.885606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1544
85.1%
1 271
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 1544
85.1%
1 271
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1544
85.1%
1 271
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1544
85.1%
1 271
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1544
85.1%
1 271
 
14.9%

DRYNESS
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1410 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1410
77.7%
1 405
 
22.3%

Length

2024-02-26T14:10:47.998555image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:48.141866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1410
77.7%
1 405
 
22.3%

Most occurring characters

ValueCountFrequency (%)
0 1410
77.7%
1 405
 
22.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1410
77.7%
1 405
 
22.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1410
77.7%
1 405
 
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1410
77.7%
1 405
 
22.3%

MASK
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1814 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1814
99.9%
1 1
 
0.1%

Length

2024-02-26T14:10:48.253293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:48.346446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1814
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1814
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1814
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1814
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1814
99.9%
1 1
 
0.1%

DARK SPOTS
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1700 
1
 
115

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

Length

2024-02-26T14:10:48.442177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:48.537505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1700
93.7%
1 115
 
6.3%

PUFFINESS
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1769 
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1769
97.5%
1 46
 
2.5%

Length

2024-02-26T14:10:48.659516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:48.755826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1769
97.5%
1 46
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 1769
97.5%
1 46
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1769
97.5%
1 46
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1769
97.5%
1 46
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1769
97.5%
1 46
 
2.5%

DARK CIRCLES
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1753 
1
 
62

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1753
96.6%
1 62
 
3.4%

Length

2024-02-26T14:10:48.852517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:48.936543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1753
96.6%
1 62
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 1753
96.6%
1 62
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1753
96.6%
1 62
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1753
96.6%
1 62
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1753
96.6%
1 62
 
3.4%

BUMPS
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1815 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1815
100.0%

Length

2024-02-26T14:10:49.030849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:49.106631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1815
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1815
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1815
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1815
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1815
100.0%

INGROWNS
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1815 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1815
100.0%

Length

2024-02-26T14:10:49.245424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:49.334349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1815
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1815
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1815
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1815
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1815
100.0%

UNEVEN TONE
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1815 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1815
100.0%

Length

2024-02-26T14:10:49.429803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:49.507805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1815
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1815
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1815
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1815
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1815
100.0%

COMBINATION
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1019 
1
796 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1019
56.1%
1 796
43.9%

Length

2024-02-26T14:10:49.634530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:49.717661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1019
56.1%
1 796
43.9%

Most occurring characters

ValueCountFrequency (%)
0 1019
56.1%
1 796
43.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1019
56.1%
1 796
43.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1019
56.1%
1 796
43.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1019
56.1%
1 796
43.9%

DRY
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1067 
1
748 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1067
58.8%
1 748
41.2%

Length

2024-02-26T14:10:49.805513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:49.886692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1067
58.8%
1 748
41.2%

Most occurring characters

ValueCountFrequency (%)
0 1067
58.8%
1 748
41.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1067
58.8%
1 748
41.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1067
58.8%
1 748
41.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1067
58.8%
1 748
41.2%

NORMAL
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1011 
1
804 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1011
55.7%
1 804
44.3%

Length

2024-02-26T14:10:49.991722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:50.073573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1011
55.7%
1 804
44.3%

Most occurring characters

ValueCountFrequency (%)
0 1011
55.7%
1 804
44.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1011
55.7%
1 804
44.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1011
55.7%
1 804
44.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1011
55.7%
1 804
44.3%

OILY
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
1076 
1
739 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1076
59.3%
1 739
40.7%

Length

2024-02-26T14:10:50.172584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:50.271558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1076
59.3%
1 739
40.7%

Most occurring characters

ValueCountFrequency (%)
0 1076
59.3%
1 739
40.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1076
59.3%
1 739
40.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1076
59.3%
1 739
40.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1076
59.3%
1 739
40.7%

SKIN_TYPE_UNKNOWN
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
1
966 
0
849 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 966
53.2%
0 849
46.8%

Length

2024-02-26T14:10:50.365542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:10:50.451991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 966
53.2%
0 849
46.8%

Most occurring characters

ValueCountFrequency (%)
1 966
53.2%
0 849
46.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 966
53.2%
0 849
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 966
53.2%
0 849
46.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 966
53.2%
0 849
46.8%

brand_product_name
Text

Distinct1810
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:50.750562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length122
Median length85
Mean length52.86832
Min length12

Characters and Unicode

Total characters95956
Distinct characters67
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1805 ?
Unique (%)99.4%

Sample

1st rowSHISEIDO FACIAL COTTON
2nd rowCLINIQUE ACNE SOLUTIONS ALL-OVER CLEARING TREATMENT OIL-FREE
3rd rowCLINIQUE ACNE SOLUTIONSâ„¢ CLARIFYING LOTION
4th rowCLINIQUE ACNE SOLUTIONSâ„¢ CLEANSING FOAM
5th rowSHISEIDO BENEFIANCE NUTRIPERFECT NIGHT CREAM
ValueCountFrequency (%)
384
 
2.7%
serum 273
 
1.9%
with 267
 
1.9%
cream 238
 
1.7%
the 220
 
1.6%
moisturizer 197
 
1.4%
face 186
 
1.3%
eye 166
 
1.2%
acid 155
 
1.1%
spf 143
 
1.0%
Other values (1866) 11854
84.2%
2024-02-26T14:10:51.218485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12306
12.8%
E 9252
 
9.6%
I 7733
 
8.1%
A 6962
 
7.3%
R 6609
 
6.9%
N 5703
 
5.9%
T 5298
 
5.5%
S 4592
 
4.8%
O 4380
 
4.6%
L 4261
 
4.4%
Other values (57) 28860
30.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81048
84.5%
Space Separator 12310
 
12.8%
Decimal Number 803
 
0.8%
Other Punctuation 642
 
0.7%
Dash Punctuation 506
 
0.5%
Math Symbol 297
 
0.3%
Other Symbol 275
 
0.3%
Control 49
 
0.1%
Final Punctuation 8
 
< 0.1%
Open Punctuation 7
 
< 0.1%
Other values (4) 11
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 9252
11.4%
I 7733
 
9.5%
A 6962
 
8.6%
R 6609
 
8.2%
N 5703
 
7.0%
T 5298
 
6.5%
S 4592
 
5.7%
O 4380
 
5.4%
L 4261
 
5.3%
C 3952
 
4.9%
Other values (20) 22306
27.5%
Decimal Number
ValueCountFrequency (%)
0 203
25.3%
1 175
21.8%
5 127
15.8%
2 88
11.0%
3 79
 
9.8%
8 57
 
7.1%
4 47
 
5.9%
7 12
 
1.5%
6 8
 
1.0%
9 7
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 190
29.6%
& 162
25.2%
% 126
19.6%
' 77
12.0%
, 30
 
4.7%
! 28
 
4.4%
/ 18
 
2.8%
: 9
 
1.4%
* 2
 
0.3%
Space Separator
ValueCountFrequency (%)
12306
> 99.9%
  2
 
< 0.1%
  2
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
â„¢ 197
71.6%
® 75
 
27.3%
° 3
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 503
99.4%
– 3
 
0.6%
Math Symbol
ValueCountFrequency (%)
+ 296
99.7%
| 1
 
0.3%
Modifier Letter
ValueCountFrequency (%)
áµ€ 1
50.0%
á´¹ 1
50.0%
Control
ValueCountFrequency (%)
49
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Modifier Symbol
ValueCountFrequency (%)
Ëš 1
100.0%
Format
ValueCountFrequency (%)
​ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81050
84.5%
Common 14906
 
15.5%

Most frequent character per script

Common
ValueCountFrequency (%)
12306
82.6%
- 503
 
3.4%
+ 296
 
2.0%
0 203
 
1.4%
â„¢ 197
 
1.3%
. 190
 
1.3%
1 175
 
1.2%
& 162
 
1.1%
5 127
 
0.9%
% 126
 
0.8%
Other values (25) 621
 
4.2%
Latin
ValueCountFrequency (%)
E 9252
11.4%
I 7733
 
9.5%
A 6962
 
8.6%
R 6609
 
8.2%
N 5703
 
7.0%
T 5298
 
6.5%
S 4592
 
5.7%
O 4380
 
5.4%
L 4261
 
5.3%
C 3952
 
4.9%
Other values (22) 22308
27.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95587
99.6%
Letterlike Symbols 197
 
0.2%
None 155
 
0.2%
Punctuation 14
 
< 0.1%
Phonetic Ext 2
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12306
12.9%
E 9252
 
9.7%
I 7733
 
8.1%
A 6962
 
7.3%
R 6609
 
6.9%
N 5703
 
6.0%
T 5298
 
5.5%
S 4592
 
4.8%
O 4380
 
4.6%
L 4261
 
4.5%
Other values (42) 28491
29.8%
Letterlike Symbols
ValueCountFrequency (%)
â„¢ 197
100.0%
None
ValueCountFrequency (%)
® 75
48.4%
É 37
23.9%
È 19
 
12.3%
Ô 18
 
11.6%
° 3
 
1.9%
  2
 
1.3%
Ç 1
 
0.6%
Punctuation
ValueCountFrequency (%)
’ 8
57.1%
– 3
 
21.4%
  2
 
14.3%
​ 1
 
7.1%
Modifier Letters
ValueCountFrequency (%)
Ëš 1
100.0%
Phonetic Ext
ValueCountFrequency (%)
áµ€ 1
50.0%
á´¹ 1
50.0%

connections_num
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6942149
Minimum0
Maximum5
Zeros58
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2024-02-26T14:10:51.350311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3461713
Coefficient of variation (CV)0.36439983
Kurtosis0.20309532
Mean3.6942149
Median Absolute Deviation (MAD)1
Skewness-0.96269877
Sum6705
Variance1.8121771
MonotonicityNot monotonic
2024-02-26T14:10:51.459705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 645
35.5%
4 512
28.2%
3 322
17.7%
2 188
 
10.4%
1 90
 
5.0%
0 58
 
3.2%
ValueCountFrequency (%)
0 58
 
3.2%
1 90
 
5.0%
2 188
 
10.4%
3 322
17.7%
4 512
28.2%
5 645
35.5%
ValueCountFrequency (%)
5 645
35.5%
4 512
28.2%
3 322
17.7%
2 188
 
10.4%
1 90
 
5.0%
0 58
 
3.2%

Interactions

2024-02-26T14:10:28.596924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:06.789911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:46.286165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:55.055435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:03.793345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:13.156208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:21.430643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:37.416492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:18.744615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:54.362745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:03.121633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:12.490516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:20.793278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:28.025987image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:37.574049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:23.739465image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:54.487658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:03.256744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:12.628547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:20.903140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:28.120518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:37.685354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:28.204735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:54.597495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:03.361915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:12.722221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:21.030362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:28.212508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:37.798000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:32.733531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:54.712433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:03.458467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:12.822390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:21.121673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:28.305345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:37.895334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:37.381061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:54.806261image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:03.567530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:12.956567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:21.233724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:28.390108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:37.997499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:41.613918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:09:54.898005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:03.690260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:13.037197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:21.336942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:10:28.488569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-02-26T14:10:38.229388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-26T14:10:38.787544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

item_skubrandproductratingloves_countreviews_numpricechild_skuitem_idsimilar_productssize_ozhierarchy_1hierarchy_2hierarchy_3SKIN_CONCERN_UNKNOWNACNEBLEMISHESOILINESSPORESFINE LINESWRINKLESDULLNESSFIRMNESSELASTICITYUNEVEN TEXTUREDRYNESSMASKDARK SPOTSPUFFINESSDARK CIRCLESBUMPSINGROWNSUNEVEN TONECOMBINATIONDRYNORMALOILYSKIN_TYPE_UNKNOWNbrand_product_nameconnections_num
0101220SHISEIDOFACIAL COTTON4.80641435232913.016.01880350P1737262735132,2031391,2191526,2698348,2031441NaNSKINCARECLEANSERSMAKEUP REMOVERS100000000000000000011110SHISEIDO FACIAL COTTON3.0
11027465CLINIQUEACNE SOLUTIONS ALL-OVER CLEARING TREATMENT OIL-FREE4.018118325719.026.0NaNP1883061027473,1677137,1027507,1592831,15928561.7SKINCARETREATMENTSBLEMISH & ACNE TREATMENTS011100000000000000011110CLINIQUE ACNE SOLUTIONS ALL-OVER CLEARING TREATMENT OIL-FREE4.0
21027473CLINIQUEACNE SOLUTIONSâ„¢ CLARIFYING LOTION4.3980461401015.021.0NaNP1883071027465,1027507,1677137,1592831,18023216.7SKINCARECLEANSERSTONERS011010010010000000011110CLINIQUE ACNE SOLUTIONSâ„¢ CLARIFYING LOTION5.0
31027507CLINIQUEACNE SOLUTIONSâ„¢ CLEANSING FOAM4.1654511361052.025.02531747P1883091027473,1027465,1677137,1592831,25317474.2SKINCARECLEANSERSFACE WASH & CLEANSERS011110000000000000001100CLINIQUE ACNE SOLUTIONSâ„¢ CLEANSING FOAM5.0
41064062SHISEIDOBENEFIANCE NUTRIPERFECT NIGHT CREAM4.3684609938.097.0NaNP2029352234102,2234110,1723865,2733681,17238571.7SKINCAREMOISTURIZERSMOISTURIZERS000001111100000000000001SHISEIDO BENEFIANCE NUTRIPERFECT NIGHT CREAM4.0
51066711CLINIQUEREDNESS SOLUTIONS SOOTHING CLEANSER4.166711997288.027.0NaNP2014391066729,1717016,2664365,2608404,14846255.0SKINCARECLEANSERSFACE WASH & CLEANSERS100000000000000000000001CLINIQUE REDNESS SOLUTIONS SOOTHING CLEANSER5.0
61066729CLINIQUEREDNESS SOLUTIONS WITH PROBIOTIC TECHNOLOGY DAILY RELIEF CREAM4.001722129590.059.0NaNP2014401066711,1717016,1484641,2531697,26643651.7SKINCAREMOISTURIZERSMOISTURIZERS100000000000000000000001CLINIQUE REDNESS SOLUTIONS WITH PROBIOTIC TECHNOLOGY DAILY RELIEF CREAM5.0
71100742FRESHBLACK TEA INSTANT PERFECTING MASK4.293467104910.096.02100642P2175132100642,562090,2656445,2728236,22823903.3SKINCAREMASKSFACE MASKS100000000000000000000001FRESH BLACK TEA INSTANT PERFECTING MASK2.0
81108836LANCÔMECRÈME RADIANCE GENTLE CLEANSING CREAMY-FOAM CLEANSER4.428619122399.038.0NaNP2179322652188,1375450,2694511,1710276,10640624.2SKINCARECLEANSERSFACE WASH & CLEANSERS100000000000000000000001LANCÔME CRÈME RADIANCE GENTLE CLEANSING CREAMY-FOAM CLEANSER4.0
91151711CLINIQUEFACE SUNSCREEN BROAD SPECTRUM SPF 503.932318804251.032.0NaNP2323271717016,2313401,2480705,2083756,479361.7SKINCARESUNSCREENFACE SUNSCREEN100000000000000000000001CLINIQUE FACE SUNSCREEN BROAD SPECTRUM SPF 503.0
item_skubrandproductratingloves_countreviews_numpricechild_skuitem_idsimilar_productssize_ozhierarchy_1hierarchy_2hierarchy_3SKIN_CONCERN_UNKNOWNACNEBLEMISHESOILINESSPORESFINE LINESWRINKLESDULLNESSFIRMNESSELASTICITYUNEVEN TEXTUREDRYNESSMASKDARK SPOTSPUFFINESSDARK CIRCLESBUMPSINGROWNSUNEVEN TONECOMBINATIONDRYNORMALOILYSKIN_TYPE_UNKNOWNbrand_product_nameconnections_num
18052677318HERBIVOREMILKY WAY 10% AHA + OAT SOOTHING EXFOLIATING SERUM4.803211963249.058.0NaNP5073292458701,2379360,2297372,2554012,25539801.0SKINCARETREATMENTSFACE SERUMS000000010011000000000001HERBIVORE MILKY WAY 10% AHA + OAT SOOTHING EXFOLIATING SERUM3.0
18062677375SUPERGOOP!100% MINERAL SUNSCREEN STARTER KIT2.36361301711.030.0NaNP5052082673077,2637171,2734606,2649788,2549970NaNSKINCAREVALUE & GIFT SETSNaN000111100000000000000001SUPERGOOP! 100% MINERAL SUNSCREEN STARTER KIT2.0
18072677383FENTY SKINTRAVEL-SIZE START'R SET: DRY SKIN EDITION4.1000936810.049.0NaNP5061532648731,2418895,2592004,2648798,2459402NaNSKINCAREVALUE & GIFT SETSNaN100000000000000000000001FENTY SKIN TRAVEL-SIZE START'R SET: DRY SKIN EDITION5.0
18082677409FENTY SKINCHERRY DUB SUPERFINE DAILY CLEANSING FACE SCRUB4.211921605302.028.0NaNP5065002677417,2418903,2731818,2418853,26199713.5SKINCARECLEANSERSNaN000010010010000000000001FENTY SKIN CHERRY DUB SUPERFINE DAILY CLEANSING FACE SCRUB3.0
18092677425FENTY SKINPORE ESSENTIALS MINI FACE MASK + TONER DUO4.750064898.026.0NaNP5065142753309,2753325,2630531,764183,2418895NaNSKINCAREVALUE & GIFT SETSNaN100000000000000000000001FENTY SKIN PORE ESSENTIALS MINI FACE MASK + TONER DUO1.0
18102677607OLEHENRIKSENBANANA BRIGHT MINERAL FACE SUNSCREEN SPF 304.466414592223.035.0NaNP5053402348431,2314243,2672731,2071751,22072311.7SKINCARESUNSCREENFACE SUNSCREEN100000000000000000000001OLEHENRIKSEN BANANA BRIGHT MINERAL FACE SUNSCREEN SPF 305.0
18112677862SUPERGOOP!UNSEEN SUNSCREEN BODY SPF 404.78019625191.042.0NaNP5061922421576,2346138,2322832,2322758,26891493.4SKINCARESUNSCREENBODY SUNSCREEN100000000000000000000001SUPERGOOP! UNSEEN SUNSCREEN BODY SPF 403.0
18122677896GLOW RECIPEPLUM PLUMP HYALURONIC ACID LIP GLOSS BALM4.499394608671.022.0NaNP5054332743250,2371524,2535128,2371516,21318030.5SKINCARELIP BALMS & TREATMENTSNaN000001110001000000000001GLOW RECIPE PLUM PLUMP HYALURONIC ACID LIP GLOSS BALM5.0
18132678233RANAVATLOTUS MAKEUP REMOVING CLEANSING BALM4.57661607111.055.0NaNP5056682643468,2564193,2564177,2522720,26859153.4SKINCARECLEANSERSMAKEUP REMOVERS100000000000000000000001RANAVAT LOTUS MAKEUP REMOVING CLEANSING BALM4.0
18142678555FACEGYMACNE LIGHT SHOT4.1852428427.090.0NaNP5051712270197,2456184,2426351,2685907,2325777NaNSKINCARETREATMENTSBLEMISH & ACNE TREATMENTS100000000000000000000001FACEGYM ACNE LIGHT SHOT2.0